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Boosting is a machine learning ensemble technique used to improve the performance of a predictive model. It works by training multiple weak models (usually decision trees) in a sequence, where each subsequent model focuses on the errors made by the previous models. The final prediction is made by combining the predictions of all the weak models weighted according to their accuracy. Boosting helps in reducing bias and variance, which in turn enhances the overall model performance. It is widely used in various applications such as image recognition, text classification, and trading algorithms. Boosting algorithms such as AdaBoost and Gradient Boosting are commonly employed for boosting tasks.

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